Evaluating AI Coding Tools: Blind A/B Tests on Real Tickets

Vendor dashboards optimize for suggestion accept rate — a vanity metric that rewards autocomplete of imports. Senior teams run blind A/B tests on real tickets: Cursor vs Claude Code vs Copilot (or Bedrock agents), measuring time-to-merge, revert rate, and review comment severity. If you cannot hide the tool brand from graders, you are measuring fashion, not productivity.

⚡ TL;DR: Sample real tickets; randomly assign tools; grade diffs blind; track TTM, reverts, blocking review comments, and escaped defects. Ignore raw accept %. Pair with multi-model routing economics, semantic diff review, and CI-surviving agent refactors.

Design a trial that survives skepticism

// eval_assign.ts
type Tool = "cursor" | "claude-code" | "copilot" | "control-no-ai";

export function assignTool(ticketId: string, salt: string): Tool {
  // ✅ stable hash assignment for reproducibility
  const h = hash(`${salt}:${ticketId}`);
  const tools: Tool[] = ["cursor", "claude-code", "copilot", "control-no-ai"];
  return tools[h % tools.length]!;
}

export type TicketOutcome = {
  ticketId: string;
  tool: Tool; // hidden from graders
  minutesToMerge: number;
  revertWithin7d: boolean;
  blockingReviewComments: number;
  escapedDefectSev: 0 | 1 | 2 | 3;
};

✅ Include a no-AI control cohort.
❌ “Engineers pick their favorite tool for the hard tickets.”

Metrics that matter (and ones that lie)

# summarize.py
import statistics as stats

def report(rows: list[dict]):
    for tool, group in groupby(rows, "tool"):
        print(tool, {
            "p50_ttm": stats.median(r["minutesToMerge"] for r in group),
            "revert_rate": mean(r["revertWithin7d"] for r in group),
            "p50_blocking": stats.median(r["blockingReviewComments"] for r in group),
        })

Blind grading: strip tool banners from PR descriptions; graders score behavioral risk using semantic diff review rubrics.

Sample size and fairness controls

# trial protocol (illustrative)
tickets: 120  # >= 30 per arm after exclusions
duration: 4 weeks
exclude:
  - pure docs / typography
  - incidents (separate study)
fairness:
  - same CODEOWNERS rules
  - same CI gates
  - no tool-specific reviewer coaching mid-trial

Budget LLM spend separately so a “winning” tool is not just the one that burned 10× tokens — see token budgets.

Closing checklist

✅ Dos
– ✅ Blind graders to tool identity
– ✅ Use real production tickets + control arm
– ✅ Track TTM, reverts, blocking comments, escaped defects
– ✅ Pre-register success criteria
– ✅ Normalize CI and ownership rules across arms

❌ Don’ts
– ❌ Don’t crown a winner by accept rate screenshots
– ❌ Don’t let engineers self-select tools on hard work
– ❌ Don’t change review standards mid-trial
– ❌ Don’t ignore cost per merged ticket
– ❌ Don’t skip qualitative notes on failure modes (authz, flaky tests)

Related reading

• Multi-Model Routing: Cheap Draft Models Plus Expensive Verifiers
• Semantic Diff Review: AI That Flags Behavioral Drift Only
• Cursor Agent Mode: Multi-File Refactors That Survive CI Gates
• LLM Cost Controls: Token Budgets Per PR and Per Engineer

Last updated on September 11, 2026